Abstract In 3D printing processes, there are many thermal stress related defects that can have a significant negative impact on the shape and size of the structure. Such anomalies in the heat transfer of the printing process need to be detected at an early stage. Understanding heat transfer is crucial, and simulation models can offer insights while reducing the need for costly experiments. Traditional numerical solvers for heat transfer can be complex to adapt to diverse printed part geometries, and their reliance on predefined mathematical models limits their flexibility. Our physics-informed deep learning (PIDL) approach eliminates the need for discretization, simplifying the analysis of complex geometries and enabling automation. The drawback of parametric PIDL is their scalability for high-dimensional problems. Computational time, energy and cost of training prevent real-time analysis. It often takes only a few seconds to print a single layer. We can show an energy efficient transfer and training strategy to reduce the computational effort of PIDL significantly. The approach is able to quantify relevant effects of thermal stresses and mitigate errors during selective laser melting (SLM). To this end, heat transfer is modelled, simulated and analysed using high-dimensional data obtained from printing experiments with different geometries of metal components. The proposed method is applied to the solving forward problem of heat transfer prediction. The governing results are based on the heat equation, which is integrated into a deep neural network (DNN).

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

MOTIVATION: Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a biochemical method for detecting interaction sites of proteins with mRNA. This method introduces T-to-C substitutions at sequenced cDNA that help to detect binding sites on mRNA. However, T-to-C substitutions can also occur due to other reasons such as mismatches or SNPs. Only few statistical procedures exist for detecting binding sites in PAR-CLIP data. Most of these methods do not account for other types of substitutions than those induced by PAR-CLIP, and therefore, also report positions with high T-to-C substitution rates, e.g. SNPs, as binding sites. Moreover, none of these procedures allow to include additional information, e.g. the type of mRNA region, relevant for the biology of microRNA-binding sites. RESULTS: We have developed BayMAP, a procedure based on a fully Bayesian hierarchical model that takes other sources of substitutions into account. Furthermore, this model enables the incorporation of additional information into the analysis of PAR-CLIP data. This incorporation does not only permit a better detection of binding sites, but also a better understanding of the data and the biology of binding sites. In applications to simulated PAR-CLIP data, BayMAP distinguishes binding sites from noise better than existing methods. Additionally, it yields good estimates of the influence of the additional information. We here demonstrate BayMAP's usability for real datasets even when noisy data is present. AVAILABILITY AND IMPLEMENTATION: BayMAP is freely available as an R package at http://stat.math.uni-duesseldorf.de/baymap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels. RESULTS: We propose a coefficient quantifying the degree to which genes present consistent alterations in multiple (more than two) genomic variables when comparing samples presenting a condition of interest (e.g. cancer) to a reference group. A hierarchical Bayesian model is employed to assess uncertainty on a gene level, incorporating information on functional relationships between genes. We demonstrate the approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histone modification measurements. Both the coefficient-based ranking and the inference based on the model lead to a plausible prioritizing of candidate genes when analyzing multiple genomic variables. AVAILABILITY AND IMPLEMENTATION: BUGS code in the Supplement. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. RESULTS: We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. AVAILABILITY AND IMPLEMENTATION: R/Bioconductor package epigenomix. CONTACT: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

MOTIVATION: The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material). RESULTS: In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in GE and CN data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches. AVAILABILITY: Code and data available as R packages edira and ediraAMLdata from http://www.statistik.tu-dortmund.de/~schaefer/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The labeling of features by synchronization of spikes seems to be a very efficient encoding scheme for a visual system. Simulation of a vision system with millions of pulse-coded model neurons, however, is almost impossible on the base of available processors including parallel processors and neurocomputers. A "one-to-one" silicon implementation of pulse-coded model neurons suffers from communication problems and low flexibility. On the other hand, acceleration of the simulation algorithm of pulse-coded leaky integrator neurons has proved to be straightforward, flexible, and very efficient. Thus we decided to develop an accelerator for a special version of the French and Stein neurons with modulatory inputs which are advantageous for simulation of synchronization mechanisms. Moreover, our accelerator also provides a Hebbian-like learning rule and supports adaptivity. Up to 128 K neurons with a total number of 16 M freely allocatable synapses are simulated within one system. The size of networks, however, is not at all limited by these numbers as the system may be arbitrarily expanded. Simulation speed obviously depends on the number of interconnections and on the average activity within the network. In the case of locally interconnected networks for simulation of vision mechanisms there is only a very low percentage of simultaneously active neurons: stimuli are not simultaneously presented in all orientations and at all positions of the visual field. In these cases our accelerator provides close to real-time behavior if one second of a biological neuron is simulated by 1000 time slots.